Multi-View Deep Matrix Factorization with Consensual Solution from Multiple Paths
2022 IEEE International Conference on Multimedia and Expo (ICME)(2022)
Abstract
Multi-view data often contain redundant information that cannot be simply spliced. Many existing methods for processing them by assigning weights to each view cannot capture features dynamically. Therefore, we propose a multi-view deep matrix factorization method via neural networks that captures semantic hierarchical information of the data and dynamically produces a consistent representation using the complementarity of multi-view features. Due to the usefulness of deep matrix factorization, the generated representation is easily interpretable. The proposed method yields a harmonized representation directly from multi-view data without an extra weight learning process. In addition, we use a multi-path network to search for a consensual solution and obtain an optimal result. Additional feature optimization is used to enhance the discriminative characterization of the representation matrix. Finally, experiments on four real-world datasets show that the proposed method is superior to state-of-the-arts.
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Key words
Deep learning,multi-view learning,deep matrix factorization,consensual solution,multiple paths
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